Analytics at the edge

Examples of opportunity in the Internet of Things

By Fiona McNeill, Global Product Marketing Manager, SAS

The data streaming in and out of organizations from electrical and mechanical sensors, RFID tags, smart meters, scanners, mobile communications, live social media, and more results in staggering volumes of information. When all these sources are networked to communicate with each other – without human intervention – the Internet of Things (IoT) is born.

The IoT market is estimated to include nearly 26 billion devices, with a “global economic value-add” of $1.9 trillion by 20201 and nearly $9 trillion in annual sales by 20202. By all accounts, IoT is a new type of industrial revolution.

But to derive useful knowledge from the tide of streaming source data – and participate in this new economy – you must have analytics.

In traditional analysis, data is stored and then analyzed. But with streaming data, analytics must occur in real time, as the data passes through. This allows you to identify and examine patterns of interest as the data is being created. The result is instant insight and immediate action.

So before the data is stored, in the cloud or in any high-performance repository, the event stream is automatically processed. And using analytics to decipher streaming data as close to the device as possible creates a new realm of knowledge for many industries.

Event streams that know more than just existing conditions, and which evaluate future scenarios using advanced analytics, are now within the realm of possibility.

Let’s look at a few examples.

The Internet of Things in health care

In health care, analyzing IoT data can result in increased uptime for machines that treat cancer, which means that patients are treated when they are scheduled. If a treatment time is missed, it can be up to 40 percent less effective, so reducing service interruptions is critical. By monitoring hundreds of sensors, identifying issues early and proactively correcting them, service personnel armed with the necessary information and parts arrive together.3 Elekta, a Swedish company that provides equipment and clinical management to help treat cancer and brain disorders, has cited a 30 percent reduction in site visits because of such monitoring.4

With rising global populations and corresponding increases in disease and health care costs, the remote patient monitoring market doubled from 2007 to 2011 and is projected to double again by 20165.

And what if these scenarios went beyond monitoring device status or patient conditions – to predicting machine reliability in advance of parts beginning to malfunction? Servicing would then move from being proactive to being optimized across each supplier’s landscape of devices. And foreseeing patient problems before they even experience symptoms could avert adverse events altogether.

Event streams that know more than just existing conditions, and which evaluate future scenarios using advanced analytics, are now within the realm of possibility.

How do you apply predictive capabilities to IoT data? High-performance analytics environments are designed to examine complex questions and produce models. These algorithms are then coded into the data streams, along with any data normalization and business rules to detect patterns associated with the defined future scenarios. So in addition to monitoring conditions and thresholds, you can use the data stream to assess likely future events.

The Internet of Things in manufacturing

The automobile industry is stepping up development detection systems for imminent collisions to determine when to take evasive action. Based on radar and other types of remote technologies, driving conditions are monitored to assess – and ultimately avoid – collisions. These collision avoidance systems assess the likelihood of a collision event and automatically prescribe mechanical changes to the vehicle if the driver doesn’t respond – including deceleration and external lighting changes. Potential accident reduction from wide deployment could surpass $100 billion annually6 in savings.

In fact, the “Industrial Internet”7 – which combines physical machinery, networked sensors and software – has extensive use and promise in manufacturing, including production optimization, product development and aftermarket servicing. GE predicts $1 trillion in opportunity per year by improving how assets and resources are used and how operations and maintenance are performed within industrial industries.8

The Internet of Things in energy

A detailed view of energy consumption patterns is needed to understand energy usage, daily spikes and workload dependencies. And beyond just manufacturing – lighting alone takes 19 percent of the world’s electricity.9 Optimized alternate energy sources can have a significant impact on all sectors.

For example, a single blade on a gas turbine can generate 500GB of data per day.10 Wind turbines constantly identify the best angles to catch the wind, and turbine-to-turbine communications allow turbine farms to align and operate as a single, maximized unit.

Historically, the only way to know what was happening with a turbine – even if it was on and working – was to climb 330 feet and see. Remote monitoring provides new eyes on the status of these energy generators.

What if the same data could be used to forecast? Efficiency in green energy means we can store more energy for use when the wind is low. Predicting when excess energy is available can help determine when to charge batteries, for example, further extending the efficiencies of alternate energy sources.

Of course, the energy market provides one of the most well-known examples of IoT technology altering the customer landscape. With dynamic smart meter billing, customers have new choices, which lead energy companies to adopt a more customer-centric approach. The utility smart grid transformation is expected to almost double the customer information system market, from $2.5 billion in 2013 to $5.5 billion in 2020.11

The Internet of Things in retail

Customers are also at the center of IoT analytics in retail, where some companies are studying ways to gather and process data from thousands of shoppers as they journey through stores. This “in-store geography” informed by sensor readings and videos considers how long shoppers linger at individual displays, recording what they ultimately buy.

With the goal of optimizing store layout, these data points can also be tied to smart-device Wi-Fi networks. In addition to appropriately targeting shoppers for promotions in-store, retailers can ask customer opinions – using IoT data to initiate an interaction, customizing the shopping experience and enhancing loyalty.

Taking action with IoT data

Event streams monitor patterns of interest. Sensors and devices generate lots of data that describes existing conditions. Analysis of conditions informs what actions are necessary – either immediately as an alert notification, or with pre-planning from predictive and other advanced analysis methods.

Of course, analysis always leads to more questions – directing what additional sensors (and data) can be collected to measure new aspects of the conditions, elements of the event or more detail in the scenario to understand different patterns.

IoT data, by itself, isn’t the value. Just as with traditional data sources, it’s the ability to take the insights and then act on them that provides value. To know what to do in the moment, use analytics at the edge.

With a background in applying analytics to real-world business scenarios, McNeill focuses on the automation of analytic insight in both business and application processing. Having been at SAS for over 15 years, she has worked with organizations across a variety of industries, understanding their business and helping them derive tangible benefit from their strategic use of technology. She is coauthor of the book Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World.